Custom AI/ML solutions for agriculture & agtech industry
Custom AI and ML for growers, agtech startups, and food processors. Crop disease detection, yield forecasting, soil sensing, and supply-chain traceability — built for farm-to-table data.
Where AI fits from field scouting to the processing line
AI models for crop health, yield planning, irrigation, processing and sustainability reporting.
Crop Disease & Pest Detection
CNN-based image classifiers running on drones, phones, and field sensors. Catches outbreaks before they spread.
Yield Forecasting
LSTM and ensemble models that forecast yield ahead of harvest to inform futures hedging and supply commitments.
Precision Irrigation & Fertilization
Per-zone water and nutrient micro-dosing from soil-sensor, weather and crop-stage signals, written as prescription maps your agronomist approves.
Drone & Satellite Field Vision
Multispectral imagery for stand counting, lodging, weed identification, and harvest readiness.
Agronomic & AgTech Research AI
RAG-grounded research-AI over USDA, extension, agronomic journals, breeding literature, and your internal trial data. For agronomy R&D, breeding programs, and AgTech innovation teams.
Sustainability & Carbon Reporting
Soil-carbon, GHG-emissions, and regenerative-practice scoring for ESG and carbon-credit programs.
Capabilities across the agriculture & agtech value chain
Crop Health & Disease Detection
- CNN-based disease and pest classifiers on drone and phone imagery
- IoT sensor anomaly detection for early infestation signs
- Weed identification and selective-spray vision
- Crop-stage and phenology recognition models
Yield & Resource Planning
- 6-month yield forecasting with LSTM and ensemble models
- Per-zone irrigation and fertilizer prescription
- Planting-density and variety-selection optimization
- Weather-stress and damage forecasting
Supply Chain & Processing
- Harvest-timing and logistics scheduling models
- Quality-grading vision for processors and packers
- Shelf-life and contamination-risk prediction
- Commodity-price and futures-hedging signals
Sustainability & Compliance
- Soil-carbon and GHG-emissions modeling
- Regenerative-practice scoring for carbon-credit programs
- Pesticide-application optimization for compliance
- Water-rights and drought-risk tracking
- Satellite- and sensor-based methane monitoring for livestock and dairy operations
How yield forecasting and field vision work for a row-crop operation
In a row-crop operation, every field is managed in zones that differ in soil, moisture and yield potential. We build yield-forecasting models and a field-vision pipeline that combine soil-moisture sensors, multispectral satellite imagery (Sentinel-2 and Planet), historical yield maps and weather forecasts into per-zone yield estimates and draft water and fertilizer prescriptions. Agronomists and farm managers open each field with the zone map and draft prescription already in place, then adjust or approve it before it goes to the planter or sprayer.
See ML modeling →Speak with an agriculture & agtech AI expert
A 30-minute scoping call. We come prepared with your acreage and crop mix, your season and yield data, and a directional read on which workflows are worth automating first.
Book a Strategy Session- CNN-based crop disease and pest detection on drone imagery
- 6-month yield forecasting with regional weather signals
- Per-zone irrigation and fertilizer prescription
- Weed identification and selective-spray vision
- Soil-carbon and regenerative-practice scoring for carbon credits
- Agronomic and AgTech research-AI (USDA + breeding literature + private trial data)
Frequently Asked Questions
Yes. We integrate with John Deere Operations Center, Climate FieldView and PTx Trimble, the AGCO joint venture that now operates the former Trimble Ag business, plus most major FMIS platforms. Equipment integration includes prescription maps in ISO XML and shapefile for Deere, Case and AGCO planters and sprayers. Prescriptions run on the equipment you already own.
Yes. We design for intermittent connectivity by default. Drone and sensor data syncs when devices return to the shop network, ML inference runs on-device or at the edge gateway, and prescription maps download to the cab once a day rather than streaming. Systems are built to run in fields with no 4G coverage.
Growers own their data. Period. All training and inference happens in your environment or single-tenant cloud you own; we don't sell or aggregate grower data to third parties. For cooperatives and processors, we offer federated learning patterns so members benefit from the network effect without exposing field-level data.
Yes. The underlying ML platform (sensor and imagery ingest, feature store, model registry, monitoring) is reusable across operation types. Domain models differ — row crops focus on yield and inputs, specialty on disease detection and quality grading, livestock on health monitoring and feed conversion — but most multi-enterprise farms run one shared stack with multiple model families.
Explore AI/ML solutions for agriculture & agtech
Private AI for Business
Private AI for business on infrastructure you control: on-premise AI, private LLMs, chat, search and RAG deployed on your servers or in your cloud tenant.
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Learn more →Self-Hosted AI for Business
Self-hosted AI for business: LibreChat, Onyx and vLLM deployed in your VPC, on-premise or air-gapped, with BYO-LLM, SSO and audit logs your team controls.
Learn more →Self-Hosted Enterprise Search
Enterprise search software in your tenant: self-hosted Onyx with permission-aware retrieval and BYO-LLM, an open-source Glean alternative on-prem or in a VPC.
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Learn more →Air-Gapped AI for Regulated Industries
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Learn more →We Build for 20 Industries
Ready to talk agriculture & agtech AI?
Start with a 30-minute strategy session. We come prepared with a directional read on your operation and a scoped proposal.